Generalizing trajectories

To reduce the size of trajectory objects, we can generalize them, for example, using the Douglas-Peucker algorithm:

import pandas as pd
import geopandas as gpd
from geopandas import GeoDataFrame, read_file
from shapely.geometry import Point, LineString, Polygon
from datetime import datetime, timedelta
import movingpandas as mpd

import warnings
warnings.filterwarnings('ignore')

print(f'MovingPandas version {mpd.__version__}')

MovingPandas version 0.8.rc1

gdf = read_file('../data/geolife_small.gpkg')
traj_collection = mpd.TrajectoryCollection(gdf, 'trajectory_id', t='t')

original_traj = traj_collection.trajectories[1]
print(original_traj)

Trajectory 2 (2009-06-29 07:02:25 to 2009-06-29 11:13:12) | Size: 897 | Length: 38764.6m
Bounds: (116.319212, 39.971703, 116.592616, 40.082514)
LINESTRING (116.590957 40.071961, 116.590905 40.072007, 116.590879 40.072027, 116.590915 40.072004, 

original_traj.plot(column='speed', linewidth=5, capstyle='round', figsize=(9,3), legend=True, vmax=20)

<AxesSubplot:>

DouglasPeuckerGeneralizer

Try different tolerance settings and observe the results in line geometry and therefore also length:

help(mpd.DouglasPeuckerGeneralizer)

Help on class DouglasPeuckerGeneralizer in module movingpandas.trajectory_generalizer:

class DouglasPeuckerGeneralizer(TrajectoryGeneralizer)
 |  DouglasPeuckerGeneralizer(traj)
 |  
 |  Generalizes using Douglas-Peucker algorithm.
 |  
 |  tolerance : float
 |      Distance tolerance
 |  
 |  Examples
 |  --------
 |  
 |  >>> mpd.DouglasPeuckerGeneralizer(traj).generalize(tolerance=1.0)
 |  
 |  Method resolution order:
 |      DouglasPeuckerGeneralizer
 |      TrajectoryGeneralizer
 |      builtins.object
 |  
 |  Methods inherited from TrajectoryGeneralizer:
 |  
 |  __init__(self, traj)
 |      Create TrajectoryGeneralizer
 |      
 |      Parameters
 |      ----------
 |      traj : Trajectory or TrajectoryCollection
 |  
 |  generalize(self, tolerance)
 |      Generalize the input Trajectory/TrajectoryCollection.
 |      
 |      Parameters
 |      ----------
 |      tolerance : any type
 |          Tolerance threshold, differs by generalizer
 |      
 |      Returns
 |      -------
 |      Trajectory/TrajectoryCollection
 |          Generalized Trajectory or TrajectoryCollection
 |  
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from TrajectoryGeneralizer:
 |  
 |  __dict__
 |      dictionary for instance variables (if defined)
 |  
 |  __weakref__
 |      list of weak references to the object (if defined)

generalized_traj = mpd.DouglasPeuckerGeneralizer(original_traj).generalize(tolerance=0.001)
generalized_traj.plot(column='speed', linewidth=5, capstyle='round', figsize=(9,3), legend=True, vmax=20)

<AxesSubplot:>

generalized_traj

Trajectory 2 (2009-06-29 07:02:25 to 2009-06-29 11:13:12) | Size: 45 | Length: 37021.3m
Bounds: (116.319212, 39.971775, 116.592616, 40.082369)
LINESTRING (116.590957 40.071961, 116.590367 40.073957, 116.590367 40.073957, 116.590367 40.073957, 

print('Original length: %s'%(original_traj.get_length()))
print('Generalized length: %s'%(generalized_traj.get_length()))

Original length: 38764.57548255216
Generalized length: 37021.32271586604

MinTimeDeltaGeneralizer

An alternative generalization method is to down-sample the trajectory to ensure a certain time delta between records:

help(mpd.MinTimeDeltaGeneralizer)

Help on class MinTimeDeltaGeneralizer in module movingpandas.trajectory_generalizer:

class MinTimeDeltaGeneralizer(TrajectoryGeneralizer)
 |  MinTimeDeltaGeneralizer(traj)
 |  
 |  Generalizes based on time.
 |  
 |  This generalization ensures that consecutive rows are at least a certain timedelta apart.
 |  
 |  tolerance : datetime.timedelta
 |      Desired minimum time difference between consecutive rows
 |  
 |  Examples
 |  --------
 |  
 |  >>> mpd.MinTimeDeltaGeneralizer(traj).generalize(tolerance=timedelta(minutes=10))
 |  
 |  Method resolution order:
 |      MinTimeDeltaGeneralizer
 |      TrajectoryGeneralizer
 |      builtins.object
 |  
 |  Methods inherited from TrajectoryGeneralizer:
 |  
 |  __init__(self, traj)
 |      Create TrajectoryGeneralizer
 |      
 |      Parameters
 |      ----------
 |      traj : Trajectory or TrajectoryCollection
 |  
 |  generalize(self, tolerance)
 |      Generalize the input Trajectory/TrajectoryCollection.
 |      
 |      Parameters
 |      ----------
 |      tolerance : any type
 |          Tolerance threshold, differs by generalizer
 |      
 |      Returns
 |      -------
 |      Trajectory/TrajectoryCollection
 |          Generalized Trajectory or TrajectoryCollection
 |  
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from TrajectoryGeneralizer:
 |  
 |  __dict__
 |      dictionary for instance variables (if defined)
 |  
 |  __weakref__
 |      list of weak references to the object (if defined)

time_generalized = mpd.MinTimeDeltaGeneralizer(original_traj).generalize(tolerance=timedelta(minutes=1))
time_generalized.plot(column='speed', linewidth=5, capstyle='round', figsize=(9,3), legend=True, vmax=20)

<AxesSubplot:>

time_generalized.to_point_gdf().head(10)

	id	sequence	trajectory_id	tracker	geometry
t
2009-06-29 07:02:25	1556	1090	2	0	POINT (116.59096 40.07196)
2009-06-29 07:03:25	1569	1103	2	0	POINT (116.59069 40.07225)
2009-06-29 07:04:25	1582	1116	2	0	POINT (116.59037 40.07396)
2009-06-29 07:05:25	1595	1129	2	0	POINT (116.59260 40.07411)
2009-06-29 07:06:25	1610	1144	2	0	POINT (116.59258 40.07420)
2009-06-29 07:07:25	1623	1157	2	0	POINT (116.59235 40.07602)
2009-06-29 07:08:25	1635	1169	2	0	POINT (116.58939 40.07794)
2009-06-29 07:09:25	1647	1181	2	0	POINT (116.58911 40.08171)
2009-06-29 07:10:25	1659	1193	2	0	POINT (116.58829 40.08232)
2009-06-29 07:11:25	1672	1206	2	0	POINT (116.58689 40.08230)

original_traj.to_point_gdf().head(10)

	id	sequence	trajectory_id	tracker	geometry
t
2009-06-29 07:02:25	1556	1090	2	0	POINT (116.59096 40.07196)
2009-06-29 07:02:30	1557	1091	2	0	POINT (116.59091 40.07201)
2009-06-29 07:02:35	1558	1092	2	0	POINT (116.59088 40.07203)
2009-06-29 07:02:40	1559	1093	2	0	POINT (116.59091 40.07200)
2009-06-29 07:02:45	1560	1094	2	0	POINT (116.59096 40.07198)
2009-06-29 07:02:50	1561	1095	2	0	POINT (116.59101 40.07196)
2009-06-29 07:02:55	1562	1096	2	0	POINT (116.59099 40.07198)
2009-06-29 07:03:00	1563	1097	2	0	POINT (116.59098 40.07199)
2009-06-29 07:03:05	1564	1098	2	0	POINT (116.59097 40.07200)
2009-06-29 07:03:10	1565	1099	2	0	POINT (116.59097 40.07200)